A probabilistic model for vehicle scheduling based on stochastic trip times

In this paper, the ant colony algorithms is studied, and improve the shortcomings of the algorithm, And the improved algorithm is introduced into the field of logistics transportation.Aiming at the complexity and uncertainty of logistics transportation vehicle scheduling problem, a new algorithm is designed.The experimental results show that the improved algorithm can choose the transport route, speed up the transportation speed, improve the service quality, reduce the transportation cost and increase economic benefits.

This paper investigates the application of a new class of neighborhood search algorithms—cyclic transfers—to multivehicle routing and scheduling problems. These algorithms exploit the two-faceted decision structure inherent to this problem class: First, assigning demands to vehicles and, second, routing each vehicle through its assigned demand stops. We describe the application of cyclic transfers to vehicle routing and scheduling problems. Then we determine the worst-case performance of these algorithms for several classes of vehicle routing and scheduling problems. Next, we develop computationally efficient methods for finding negative cost cyclic transfers. Finally, we present computational results for three diverse vehicle routing and scheduling problems, which collectively incorporate a variety of constraint and objective function structures. Our results show that cyclic transfer methods are either comparable to or better than the best published heuristic algorithms for several complex and important vehicle routing and scheduling problems. Most importantly, they represent a novel approach to solution improvement which holds promise in many vehicle routing and scheduling problem domains.

Economic lot sizes and vehicle scheduling

This paper examines time windows for scheduled trips in multiple depot vehicle and crew scheduling problems that arise in public bus transportation. In practice, the two planning tasks vehicle scheduling and crew scheduling are traditionally solved sequentially with the implicit understanding that the scheduled time for timetabled trips remains fixed. In order to improve cost efficiency two concepts have been developed over the last years: In order to obtain better flexibility when scheduling crews, vehicle and crew scheduling problems are tackled simultaneously. In order to extend flexibility while scheduling vehicles, variable trip departure and arrival times are considered. Obviously the combination of both concepts promises the largest savings, but probably leads to bursting computational times due to growing problem complexity. In this paper we combine both concepts by extending the integrated vehicle and crew scheduling problem with the possibility to shift scheduled trips within defined time windows. We examine the tradeoffs between solution quality and computational time for different solution approaches.

The vehicle scheduling problem with intermittent customer demands

Vehicle scheduling under stochastic trip times: An approximate dynamic programming approach

This thesis goes deep into the vehicle scheduling problem (VSP), which is the key problem for the distribution center.This paper analyzes and optimizes the mathematical model of vehicle scheduling problem. The problem of dynamic vehicle scheduling with time windows is described in great details in the thesis, which also gives an arithmetic solution aiming at the scheduling problem.Finally, based on the research results and under the background of logistics distribution enterprises, the vehicle scheduling algorithm is exposed to experiment.

Optimisation of vehicle schedule with time windows is discussed in this paper. The problem is based on the description and data of a real-life problem. Simulation-based optimisation is used in the search of an optimal solution in this paper. A simulation model is developed for the reviewed vehicles scheduling problem. Two scenarios of optimisation are described. The first scenario involves the use of OptQuest software for the optimisation. In the second scenario, the optimisation uses an evolutionary algorithm and preliminary analysis of the problem complexity. Fitness landscape analysis methods are proposed to enhance vehicle schedule optimisation. The fitness landscape analysis is performed for the vehicle scheduling problem with time windows. The procedure of simulation-based fitness landscape analysis is described.

The model and algorithm for Vehicle Routing and Scheduling Problem (VRSP) in picking up and delivering customers to airport service were presented in this paper. VRSP is an extension of the class Vehicle Routing Problem (VRP) with more resource constraints. Mathematical models are described for the VRSP with minimizing the cost based on low carbon emissions. The Bi-directional Optimization Heuristic Algorithm (BOHA) is developed for this model. The results verified the effectiveness of the model and Algorithm. The proposed model and algorithm can provide quantitative support and help the air ticket company decision-maker develop economical, punctual shuttle service with high satisfaction.

Multi-depot vehicle scheduling with multiple vehicle types on overlapped bus routes

Optimal Multi-Vehicle Type Transit Timetabling and Vehicle Scheduling

One of the problems in public transportation is the vehicle scheduling problem (VSP), which can reduce the bus company cost and meet the demand of passengers’ minimum waiting time. This paper proposes an ensemble differential algorithm based on particle swarm optimization (abbreviated as PSOEDE) to solve the VSP. In PSOEDE algorithm, the mutation process is designed by dividing the original process into two parts: the first part combines the PSO operator with the improved mutation strategy to enhance the global search ability, while the second part is to randomly select two mutation strategies (i.e. random learning and optimal learning) to improve the diversity of population. In addition, the random selection methods of the parameters and crossover strategies are proposed and applied in the total PSOEDE algorithm. The effectiveness and superiority of the proposed PSOEDE algorithm in dealing with the VSP are verified using the simulation experiments and six comparison algorithms.

The coordinated development of companies and ecological protection are possible only with increasing environmental awareness. Therefore, this study aims to investigate how companies can achieve sustainable development. It is found that the scientific implementation of the vehicle scheduling problem (VSP) for just-in-time (JIT) delivery in the raw material procurement logistics of iron and steel companies can reduce the carbon emissions in the VSP process and, taking into account the negative correlation between weather conditions and PM10, can effectively reduce PM10. On this basis, a multiobjective optimization model is constructed with the objectives of minimizing carbon emissions and PM10 along with the traditional objective of cost optimization. A greedy algorithm with high computational efficiency and an embedded genetic algorithm (GA) is used to further improve the response time of the VSP. Verification shows that in practice, the model enables companies to effectively reduce not only logistics costs but also PM10 and carbon emissions; in theory, the model expands the applicability of JIT to all value-added activities, exploring all value-added activities in different spatial and temporal dimensions to achieve the optimal combination of company cost, environmental effects, and weather dimensions.

A comparison of different solution approaches to the vehicle scheduling problem in a practical case

Insertion heuristics have proven to be popular methods for solving a variety of vehicle routing and scheduling problems. In this paper, we focus on the impact of incorporating complicating constraints on the efficiency of insertion heuristics. The basic insertion heuristic for the standard vehicle routing problem has a time complexity of O(n3). However, straightforward implementations of handling complicating constraints lead to an undesirable time complexity of O(n4). We demonstrate that with careful implementation it is possible, in most cases, to maintain the O(n3) complexity or, in a few cases, increase the time complexity to O(n3 log n). The complicating constraints we consider in this paper are time windows, shift time limits, variable delivery quantities, fixed and variable delivery times, and multiple routes per vehicle. Little attention has been given to some of these complexities (with time windows being the notable exception), which are common in practice and have a significant impact on the feasibility of a schedule as well as the efficiency of insertion heuristics.